


import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :			int        =							"""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))

       print('Googling.....')

       _SCREAMING_SNAKE_CASE     :			Tuple        =							f'''https://www.google.com/search?q={query}&num=100'''

       _SCREAMING_SNAKE_CASE     :			List[Any]        =							requests.get(
           url,
           headers={'User-Agent': str(UserAgent().random)},
       )

       try:
              _SCREAMING_SNAKE_CASE     :			Union[str, Any]        =							(
                  BeautifulSoup(res.text, 'html.parser')
                  .find('div', attrs={'class': 'yuRUbf'})
                  .find('a')
                  .get('href')
              )

       except AttributeError:
              _SCREAMING_SNAKE_CASE     :			int        =							parse_qs(
                  BeautifulSoup(res.text, 'html.parser')
                  .find('div', attrs={'class': 'kCrYT'})
                  .find('a')
                  .get('href')
              )["""url"""][0]

       webbrowser.open(link)


"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
